解决动态数据中分布漂移问题，提升模型鲁棒性与异常检测能力
Temp-SCONE: A Novel Out-of-Distribution Detection and Domain Generalization Framework for Wild Data with Temporal Shift
- 基于平均阈值置信度设计时序正则化损失，稳定跨时间预测
- 在动态数据上显著提升误分类准确率与异常检测可靠性
- 适合应对真实世界中持续变化的数据环境，如监控、自动驾驶
开放世界学习（OWL）要求模型能适应不断变化的环境，并可靠地识别分布外（OOD）输入。现有方法如SCONE虽对协变量和语义漂移具有鲁棒性，但假设环境静态，导致在动态场景下性能下降。本文提出Temp-SCONE，一种时序一致的SCONE扩展框架，用于处理动态环境中的时间漂移。Temp-SCONE引入基于平均阈值置信度（ATC）的置信度驱动正则化损失，惩罚时间步间预测的不稳定性，同时保持SCONE的能量边界分离特性。在动态数据集上的实验表明，Temp-SCONE在时间漂移下显著提升了鲁棒性，相较于SCONE实现了更高的损坏数据准确率和更可靠的OOD检测。在无时间连续性的独立数据集上，Temp-SCONE仍保持相当性能，凸显时序正则化的必要性与局限性。理论分析进一步揭示了时序稳定性与泛化误差的关系，推动了动态演化环境中可靠OWL的发展。
原文摘要 · Abstract (English)
Open-world learning (OWL) requires models that can adapt to evolving environments while reliably detecting out-of-distribution (OOD) inputs. Existing approaches, such as SCONE, achieve robustness to covariate and semantic shifts but assume static environments, leading to degraded performance in dynamic domains. In this paper, we propose Temp-SCONE, a temporally consistent extension of SCONE designed to handle temporal shifts in dynamic environments. Temp-SCONE introduces a confidence-driven regularization loss based on Average Thresholded Confidence (ATC), penalizing instability in predictions across time steps while preserving SCONE's energy-margin separation. Experiments on dynamic datasets demonstrate that Temp-SCONE significantly improves robustness under temporal drift, yielding higher corrupted-data accuracy and more reliable OOD detection compared to SCONE. On distinct datasets without temporal continuity, Temp-SCONE maintains comparable performance, highlighting the importance and limitations of temporal regularization. Our theoretical insights on temporal stability and generalization error further establish Temp-SCONE as a step toward reliable OWL in evolving dynamic environments.
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